





"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__UpperCamelCase     :						Any						       =  logging.get_logger(__name__)

__UpperCamelCase     :						List[str]						       =  OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

__UpperCamelCase     :						Optional[int]						       =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
  if class_name in extractors:
   lowerCAmelCase    				= model_type_to_module_name(_UpperCAmelCase			)

   lowerCAmelCase    				= importlib.import_module(F'.{module_name}'  ,     'transformers.models'			)
   try:
    return getattr(_UpperCAmelCase  ,     _UpperCAmelCase			)
   except AttributeError:
    continue

 for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
  if getattr(_UpperCAmelCase  ,     '__name__'  ,     _UpperCAmelCase			) == class_name:
   return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
 lowerCAmelCase    				= importlib.import_module('transformers'			)
 if hasattr(_UpperCAmelCase  ,     _UpperCAmelCase			):
  return getattr(_UpperCAmelCase  ,     _UpperCAmelCase			)

 return None

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, os.PathLike]  ,     _UpperCAmelCase					:    Optional[Union[str, os.PathLike]] = None  ,     _UpperCAmelCase					:    bool = False  ,     _UpperCAmelCase					:    bool = False  ,     _UpperCAmelCase					:    Optional[Dict[str, str]] = None  ,     _UpperCAmelCase					:    Optional[Union[bool, str]] = None  ,     _UpperCAmelCase					:    Optional[str] = None  ,     _UpperCAmelCase					:    bool = False  ,     **_UpperCAmelCase					:    List[Any]  ,     ):
 lowerCAmelCase    				= get_file_from_repo(
     _UpperCAmelCase  ,     _UpperCAmelCase  ,     cache_dir=_UpperCAmelCase  ,     force_download=_UpperCAmelCase  ,     resume_download=_UpperCAmelCase  ,     proxies=_UpperCAmelCase  ,     use_auth_token=_UpperCAmelCase  ,     revision=_UpperCAmelCase  ,     local_files_only=_UpperCAmelCase  ,     )
 if resolved_config_file is None:
  logger.info(
      'Could not locate the image processor configuration file, will try to use the model config instead.'			)
  return {}

 with open(_UpperCAmelCase  ,     encoding='utf-8'			) as reader:
  return json.load(_UpperCAmelCase			)







class       a							:



 def __init__(     self     ):







  """simple docstring"""






  raise EnvironmentError(
      'AutoImageProcessor is designed to be instantiated '
      'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'     )



 @classmethod
 @replace_list_option_in_docstrings(_snake_case     )
 def    UpperCamelCase__  (     cls				,       _snake_case				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= kwargs.pop('config'				,       _snake_case     )
  lowerCAmelCase    				= kwargs.pop('trust_remote_code'				,       _snake_case     )
  lowerCAmelCase    				= True

  lowerCAmelCase				,lowerCAmelCase    				= ImageProcessingMixin.get_image_processor_dict(_snake_case				,       **_snake_case     )
  lowerCAmelCase    				= config_dict.get('image_processor_type'				,       _snake_case     )
  lowerCAmelCase    				= None
  if "AutoImageProcessor" in config_dict.get('auto_map'				,       {}     ):
   lowerCAmelCase    				= config_dict['auto_map']['AutoImageProcessor']

  # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
  # and if so, infer the image processor class from there.
  if image_processor_class is None and image_processor_auto_map is None:
   lowerCAmelCase    				= config_dict.pop('feature_extractor_type'				,       _snake_case     )
   if feature_extractor_class is not None:
    logger.warning(
        'Could not find image processor class in the image processor config or the model config. Loading'
        ' based on pattern matching with the model\'s feature extractor configuration.'     )
    lowerCAmelCase    				= feature_extractor_class.replace('FeatureExtractor'				,       'ImageProcessor'     )
   if "AutoFeatureExtractor" in config_dict.get('auto_map'				,       {}     ):
    lowerCAmelCase    				= config_dict['auto_map']['AutoFeatureExtractor']
    lowerCAmelCase    				= feature_extractor_auto_map.replace('FeatureExtractor'				,       'ImageProcessor'     )
    logger.warning(
        'Could not find image processor auto map in the image processor config or the model config.'
        ' Loading based on pattern matching with the model\'s feature extractor configuration.'     )

        # If we don't find the image processor class in the image processor config, let's try the model config.
  if image_processor_class is None and image_processor_auto_map is None:
   if not isinstance(_snake_case				,       _snake_case     ):
    lowerCAmelCase    				= AutoConfig.from_pretrained(_snake_case				,       **_snake_case     )
   # It could be in `config.image_processor_type``
   lowerCAmelCase    				= getattr(_snake_case				,       'image_processor_type'				,       _snake_case     )
   if hasattr(_snake_case				,       'auto_map'     ) and "AutoImageProcessor" in config.auto_map:
    lowerCAmelCase    				= config.auto_map['AutoImageProcessor']

  if image_processor_class is not None:
   lowerCAmelCase    				= image_processor_class_from_name(_snake_case     )

  lowerCAmelCase    				= image_processor_auto_map is not None
  lowerCAmelCase    				= image_processor_class is not None or type(_snake_case     ) in IMAGE_PROCESSOR_MAPPING
  lowerCAmelCase    				= resolve_trust_remote_code(
      _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     )

  if has_remote_code and trust_remote_code:
   lowerCAmelCase    				= get_class_from_dynamic_module(
       _snake_case				,       _snake_case				,       **_snake_case     )
   lowerCAmelCase    				= kwargs.pop('code_revision'				,       _snake_case     )
   if os.path.isdir(_snake_case     ):
    image_processor_class.register_for_auto_class()
   return image_processor_class.from_dict(_snake_case				,       **_snake_case     )
  elif image_processor_class is not None:
   return image_processor_class.from_dict(_snake_case				,       **_snake_case     )
  # Last try: we use the IMAGE_PROCESSOR_MAPPING.
  elif type(_snake_case     ) in IMAGE_PROCESSOR_MAPPING:
   lowerCAmelCase    				= IMAGE_PROCESSOR_MAPPING[type(_snake_case     )]
   return image_processor_class.from_dict(_snake_case				,       **_snake_case     )

  raise ValueError(
      F'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
      F'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
      F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()     )}'     )



 @staticmethod
 def    UpperCamelCase__  (     _snake_case				,       _snake_case     ):







  """simple docstring"""






  IMAGE_PROCESSOR_MAPPING.register(_snake_case				,       _snake_case     )






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="levit"






     def __init__(					self						:						Optional[int]    ,							a						:						int=2_24    ,							a						:						Union[str, Any]=3    ,							a						:						List[Any]=3    ,							a						:						Dict=2    ,							a						:						Tuple=1    ,							a						:						Optional[int]=16    ,							a						:						Optional[Any]=[1_28, 2_56, 3_84]    ,							a						:						Dict=[4, 8, 12]    ,							a						:						Optional[Any]=[4, 4, 4]    ,							a						:						Optional[int]=[16, 16, 16]    ,							a						:						Any=0    ,							a						:						List[Any]=[2, 2, 2]    ,							a						:						Optional[Any]=[2, 2, 2]    ,							a						:						Union[str, Any]=0.02    ,							**a						:						Optional[int]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    kernel_size
         __lowerCamelCase            =    stride
         __lowerCamelCase            =    padding
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    num_attention_heads
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    key_dim
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    patch_size
         __lowerCamelCase            =    attention_ratio
         __lowerCamelCase            =    mlp_ratio
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    [
             ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
             ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
         ]




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Tuple				=version.parse("1.11"					)






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         return 1e-4
